When Is ''Nearest Neighbor'' Meaningful?

Basic local alignment search tool

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Color indexing

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Multivariate Density Estimation

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Texture features for browsing and retrieval of image data

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Adaptive Control Processes: A Guided Tour

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Efficient similarity search in sequence databases

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Visual learning and recognition of 3-d objects from appearance

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Using discriminant eigenfeatures for image retrieval

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Efficient and effective Querying by Image Content

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V. Adaptive Control Processes

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Data mining: concepts and techniques

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Survey of Clustering Algorithms

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

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Locality-sensitive hashing scheme based on p-stable distributions

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Product Quantization for Nearest Neighbor Search

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A Survey of Clustering Data Mining Techniques

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DBSCAN Revisited, Revisited

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A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography

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On the Surprising Behavior of Distance Metrics in High Dimensional Space

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Graph based anomaly detection and description: a survey

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Discovering similar multidimensional trajectories

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SMOTE for high-dimensional class-imbalanced data

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Clustering high-dimensional data

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Outlier detection for high dimensional data

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Discovering cluster-based local outliers

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Principles of Data Mining

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Machine learning in wireless sensor networks: Algorithms, strategies, and applications

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The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

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Angle-based outlier detection in high-dimensional data

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A survey on unsupervised outlier detection in high‐dimensional numerical data

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Searching in high-dimensional spaces

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Defining cell types and states with single-cell genomics

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State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

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Data Clustering

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The Curse of Dimensionality in Data Mining and Time Series Prediction

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Efficient k-nearest neighbor graph construction for generic similarity measures

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Feature bagging for outlier detection

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Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications

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On k -anonymity and the curse of dimensionality

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The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

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iDistance

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Recent advances in visual and infrared face recognition—a review

Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2004) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.04.001 · Cited by 587

Identification of cell types from single-cell transcriptomes using a novel clustering method

Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559

Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

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Finding generalized projected clusters in high dimensional spaces

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The Challenges of Clustering High Dimensional Data

Steinbach, M. S., Ertöz, L., & Kumar, V. (2004) · Springer eBooks · doi:10.1007/978-3-662-08968-2_16 · Cited by 518

K-means properties on six clustering benchmark datasets

Fränti, P., & Sieranoja, S. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1238-7 · Cited by 517

Efficient Water Quality Prediction Using Supervised Machine Learning

Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019) · Water · doi:10.3390/w11112210 · Cited by 518

Clustering by pattern similarity in large data sets

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Uncovering the structure of self-regulation through data-driven ontology discovery

Eisenberg, I. W., Bissett, P. G., Enkavi, A. Z., Li, J. K., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019) · Nature Communications · doi:10.1038/s41467-019-10301-1 · Cited by 509

Fast Discriminative Visual Codebooks using Randomized Clustering Forests

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A comprehensive survey of anomaly detection techniques for high dimensional big data

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Unified Video Annotation via Multigraph Learning

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Unsupervised Learning Methods for Molecular Simulation Data

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The pyramid-technique

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Feature selection for clustering - a filter solution

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Eliminating Fuzzy Duplicates in Data Warehouses

Ananthakrishna, R., Chaudhuri, S., & Ganti, V. (2002) · Elsevier eBooks · doi:10.1016/b978-155860869-6/50058-5 · Cited by 386

HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

Keller, F., Müller, E., & Böhm, K. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.88 · Cited by 383

Water quality prediction using machine learning models based on grid search method

Shams, M. Y., Elshewey, A. M., El‐kenawy, E. M., Ibrahim, A. F., Talaat, F. M., & Tarek, Z. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16737-4 · Cited by 383

Conditional Anomaly Detection

Song, X., Wu, M., Jermaine, C., & Ranka, S. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.1009 · Cited by 400

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019) · Academic Pathology · doi:10.1177/2374289519873088 · Cited by 381

An Investigation of Practical Approximate Nearest Neighbor Algorithms

Liu, T., Moore, A., Yang, K., & Gray, A. (2004) · Cited by 375

Efficient similarity search and classification via rank aggregation

Fagin, R., Kumar, R., & Sivakumar, D. (2003) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/872757.872795 · Cited by 365

Outlier mining in large high-dimensional data sets

Angiulli, F., & Pizzuti, C. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.31 · Cited by 368

Multi-omics integration in biomedical research – A metabolomics-centric review

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Clustering Algorithms in Biomedical Research: A Review

Xu, R., & Wunsch, D. C. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2083647 · Cited by 367

EEG-based workers' stress recognition at construction sites

Jebelli, H., Hwang, S., & Lee, S. H. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.05.027 · Cited by 350

Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering

Hinneburg, A., & Keim, D. A. (1999) · KOPS (University of Konstanz) · Cited by 339

The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering

Li, T., & Ding, C. H. Q. (2006) · Proceedings · doi:10.1109/icdm.2006.160 · Cited by 327

Toward a Quantitative Survey of Dimension Reduction Techniques

Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S. T., & Telea, A. C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2944182 · Cited by 348

The Concentration of Fractional Distances

François, D., Vincent Wertz, B., & Verleysen, M. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.1037 · Cited by 303

Classifying large data sets using SVMs with hierarchical clusters

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Locality sensitive hashing: A comparison of hash function types and querying mechanisms

Paulevé, L., Jeǵou, H., & Amsaleg, L. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.04.004 · Cited by 298

Injecting utility into anonymized datasets

Kifer, D., & Gehrke, J. E. (2006) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1142473.1142499 · Cited by 298

Independent quantization: an index compression technique for high-dimensional data spaces

Berchtold, S., Böhm, C., Jagadish, H. V., Kriegel, H.-P., & Sander, J. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2000.839456 · Cited by 290

Randomized Clustering Forests for Image Classification

Moosmann, F., Nowak, E., & Jurie, F. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70822 · Cited by 296

Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study

Mangalathu, S., & Jeon, J. (2019) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002402 · Cited by 283

Adaptive dimension reduction using discriminant analysis and K -means clustering

Ding, C. H. Q., & Li, T. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273562 · Cited by 279

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taïar, R., Hancock, P. A., & Aljuaid, A. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 295

Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?

Houle, M. E., Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13818-8_34 · Cited by 276

RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates

Bryant, A. C., & Cios, K. J. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2787640 · Cited by 279

A brief survey of machine learning methods and their sensor and IoT applications

Shanthamallu, U. S., Spanias, A., Tepedelenlioğlu, C., & Stanley, M. (2017) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2017.8316459 · Cited by 281

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang, L., Ye, H., & Li, G. Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2872122 · Cited by 282

Aggregate nearest neighbor queries in spatial databases

Dimitris Papadias, C., Tao, Y., Mouratidis, K., & Hui, C. K. (2005) · ACM Transactions on Database Systems · doi:10.1145/1071610.1071616 · Cited by 268

Dynamic query tools for time series data sets: Timebox widgets for interactive exploration (2004)

Hochheiser, H., & Shneiderman, B. (2005) · University Libraries (University of Maryland) · Cited by 268

Group nearest neighbor queries

Dimitris Papadias, C., Shen, Q., Tao, Y., & Mouratidis, K. (2004) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2004.1320006 · Cited by 258

Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

Aljarah, I., Al-Zoubi, A. M., Faris, H., Hassonah, M. A., Mirjalili, S., & Saadeh, H. (2018) · Cognitive Computation · doi:10.1007/s12559-017-9542-9 · Cited by 253

Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection

Radovanović, M., Νανόπουλος, Α., & Ivanović, M. D. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2365790 · Cited by 254

Latest developments in gear defect diagnosis and prognosis: A review

Kumar, A., Gandhi, C. P., Zhou, Y., Kumar, R., & Xiang, J. (2020) · Measurement · doi:10.1016/j.measurement.2020.107735 · Cited by 252

Space-time tradeoffs for approximate nearest neighbor searching

Arya, S., Malamatos, T., & Mount, D. M. (2009) · Journal of the ACM · doi:10.1145/1613676.1613677 · Cited by 243

Evaluating clustering in subspace projections of high dimensional data

Müller, E., Günnemann, S., Assent, I., & Seidl, T. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687770 · Cited by 247

Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

Visani, G., Bagli, E., Chesani, F., Poluzzi, A., & Capuzzo, D. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1865846 · Cited by 243

Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration

Hochheiser, H., & Shneiderman, B. (2004) · Information Visualization · doi:10.1057/palgrave.ivs.9500061 · Cited by 243

Top- k selection queries over relational databases

Bruno, N., Chaudhuri, S., & Gravano, L. (2002) · ACM Transactions on Database Systems · doi:10.1145/568518.568519 · Cited by 242

Identifying cell populations with scRNASeq

Andrews, T. S., & Hemberg, M. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.002 · Cited by 247

Study and evaluation of different Fourier methods for image retrieval

Zhang, D., & Lu, G. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.09.001 · Cited by 234

Text classification method based on self-training and LDA topic models

Pavlinek, M., & Podgorelec, V. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.020 · Cited by 238

Clustering of time series subsequences is meaningless: implications for previous and future research

Keogh, E., Lin, J., & Truppel, W. (2004) · Proceedings · doi:10.1109/icdm.2003.1250910 · Cited by 233

Quality and efficiency in high dimensional nearest neighbor search

Tao, Y., Yi, K., Sheng, C., & Kalnis, P. (2009) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1559845.1559905 · Cited by 230

Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces

Chakrabarti, K., & Mehrotra, S. (2000) · Cited by 230

Optimal multimodal fusion for multimedia data analysis

Wu, Y., Chang, E. Y., Chang, K. C.-C., & Smith, J. R. (2004) · Proceedings of the 12th annual ACM international conference on Multimedia · doi:10.1145/1027527.1027665 · Cited by 229

Cluster-based outlier detection

Duan, L., Xu, L., Liu, Y., & Lee, J. (2008) · Annals of Operations Research · doi:10.1007/s10479-008-0371-9 · Cited by 228

A Review on Time Series Aggregation Methods for Energy System Models

Hoffmann, M., Kotzur, L., Stolten, D., & Robinius, M. (2020) · Energies · doi:10.3390/en13030641 · Cited by 227

Density Sensitive Hashing

Jin, Z., Li, C., Lin, Y., & Cai, D. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2283497 · Cited by 226

Clustering through decision tree construction

Liu, B., Xia, Y., & Yu, P. S. (2000) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/354756.354775 · Cited by 226

Top-k Query Evaluation with Probabilistic Guarantees

Theobald, M., Weikum, G., & Schenkel, R. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50058-9 · Cited by 225

Hierarchical Neural Networks for Image Interpretation

Behnke, S. (2003) · Lecture notes in computer science · doi:10.1007/b11963 · Cited by 227

Closest pair queries in spatial databases

Corral, A., Manolopoulos, Y., Theodoridis, Y., & Vassilakopoulos, M. G. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335414 · Cited by 225

On the k-NN performance in a challenging scenario of imbalance and overlapping

García, V., Mollineda, R. A., & Sánchez, J. S. (2007) · Pattern Analysis and Applications · doi:10.1007/s10044-007-0087-5 · Cited by 226

Survey of vector database management systems

Pan, J. J., Wang, J., & Li, G. (2024) · The VLDB Journal · doi:10.1007/s00778-024-00864-x · Cited by 221

When Is Nearest Neighbors Indexable?

Shaft, U., & Ramakrishnan, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30570-5_11 · Cited by 22

Theory of nearest neighbors indexability

Shaft, U., & Ramakrishnan, R. (2006) · ACM Transactions on Database Systems · doi:10.1145/1166074.1166077 · Cited by 52

Study on dimensionality curse in the nearest neighbor queries based on statistics

Bo, S., Li, S., & Chong-guang, Z. (2006) · Jisuanji gongcheng

Two algorithms for nearest-neighbor search in high dimensions

Kleinberg, J. M. (1997) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/258533.258653 · Cited by 347

Density-based indexing for approximate nearest-neighbor queries

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